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Protein Folding

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Machine Learning: How Much Does It Tell about Protein Folding Rates?

PloS one
The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been ...

Protein folds recognized by an intelligent predictor based-on evolutionary and structural information.

Journal of computational chemistry
Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks inc...

Accurate contact predictions using covariation techniques and machine learning.

Proteins
Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effectiv...

Machine Learnable Fold Space Representation based on Residue Cluster Classes.

Computational biology and chemistry
MOTIVATION: Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using sim...

Types and effects of protein variations.

Human genetics
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...

Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.

Journal of theoretical biology
Enzyme catalysis is one of the most essential and striking processes among of all the complex processes that have evolved in living organisms. Enzymes are biological catalysts, which play a significant role in industrial applications as well as in me...

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence.

The Journal of chemical physics
Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the bio...

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Methods in molecular biology (Clifton, N.J.)
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularl...

DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence.

Bioinformatics (Oxford, England)
MOTIVATION: Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering...